System and method of biometric enrollment and verification

ABSTRACT

A system and method for biometric enrollment and verification compares a test biometric image (e.g., of a fingerprint) with each of a plurality of reference biometric images of one or more enrolled users. Verification of a user as an enrolled user is based on the cumulative amount of overlap between the test image and the reference images. The reference images are defined during an enrollment process by comparing a plurality of sample images, identifying overlapping data in each of the images, computing one or more quality measures, and storing at least a portion of the sample images. The enrollment process is deemed complete when each quality measures meets or exceeds an associated threshold.

CROSS REFERENCE OF RELATED APPLICATION

This application is divisional application claiming the benefit under 35U.S.C. § § 120, 121 of the filing date of non-provisional patentapplication Ser. No. 14/789,331 filed Jul. 1, 2015, the disclosure ofwhich is incorporated herein by reference.

FIELD OF THE DISCLOSURE

This disclosure relates to systems and methods for enrolling biometricdata in an enrollment database and for comparing the enrolled biometricdata—called reference biometric data or information—with test biometricdata or information for verifying the enrollment status of the test

BACKGROUND

Biometric systems are commonly used as an aid to confirmingauthorization for access to various types of resources or locations.Biometric systems measure various unique or nearly uniquecharacteristics of a person's body to assist in confirming identity and,consequently, in authorizing an access requested by the person. The bodycharacteristics, or biometric information, are measured by a biometricsensor, for example, a fingerprint sensor or an eye retinal scanner.

For the biometric system to authorize user access to a resource orlocation, the biometric information of the user has to be known by thebiometric system and the biometric system has to verify the biometricinformation of the user when the user requests authorization. For theuser to be known to the biometric system, the user will have to registeror enrol its biometric information with the system. This process isoften referred to as enrolment or registration. In the enrollmentprocess, the biometric system receives biometric information from thebiometric sensor and stores at least a portion of the biometricinformation to create a database of the biometric information. Whenverifying the user, the biometric system compares subsequently-receivedbiometric information to the biometric information stored in thedatabase, and if a sufficient match between the two is found the user isauthorized to access the resource or location.

To enable biometric sensors to be incorporated onto smallerdevices—e.g., smart phones—without taking up too much space on thedevice, and to minimize the costs of the sensor, sensors have becomesmaller and smaller. Thus, for example, a fingerprint sensor may imageonly a relatively small part of the user's finger, and the resultingfingerprint image is much smaller than the overall size of the fingersurface. Some known methods reconstruct multiple smaller images into alarge reference image. However, as images reconstruction is a lossyprocess due to e.g. elastic deformation of the fingerprint and limitedsensor resolution, this introduces errors in the reference image. Theenrollment and verification system will be able to verify the user onlyif data corresponding to the fingerprint image, i.e., the test image, isstored in the reference database in one or more of the reference images.If the test image corresponds to one part of the user's finger and thestored reference image(s) correspond to a different, non-overlappingpart of the user's finger, the user cannot be verified.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview of the claimed subject matter. It is intended toneither identify key or critical elements of the claimed subject matternor delineate the scope thereof. Its sole purpose is to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Aspects of the disclosure are embodied in a biometric identificationmethod comprising storing a plurality of reference biometric images ofan organic tissue of a user in a reference database, wherein each of thereference biometric images has a predefined image size and at leastpartially overlaps at least one of the other reference biometric images,and wherein all of the reference biometric images arranged with theiroverlapping portions aligned has an area greater than the predefinedimage size.

According to further aspects of the disclosure, storing the referencebiometric images comprises providing a plurality of sample biometricimages of the predefined image size from the user, comparing each of thesample biometric images with the other sample biometric images toidentify overlapping data in the sample biometric images, computing anamount of unique, non-overlapping data in the sample biometric images;computing an amount of unique data relative to the predefined imagesize; arranging the plurality of biometric images with their overlappingportions aligned and computing the area of a bounding borderencompassing the arranged biometric images relative to the predefinedimage size, and storing at least a portion of the plurality of samplebiometric images as a plurality of reference biometric images in thereference database.

According to further aspects of the disclosure, the plurality ofreference biometric images stored in the reference database comprises ofnumber of biometric images that results in the amount of unique datarelative to the predefined image size being equal to or greater than afirst predefined threshold, and the area of the bounding borderencompassing the arranged biometric images relative to the predefinedimage size being equal to or greater than a second predefined threshold.

According to further aspects of the disclosure, the method furthercomprises the step of computing compactness of the plurality ofreference biometric images as the amount of unique data relative to thepredefined image size divided by the area of the bounding borderencompassing the arranged biometric images relative to the predefinedimage size.

According to further aspects of the disclosure, the method furthercomprises the step of comparing the compactness with a third predefinedthreshold.

According to further aspects of the disclosure, providing the samplebiometric images comprises generating the sample biometric images with abiometric sensor.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface, and each reference biometric image comprisesa fingerprint image, a feature set corresponding to the fingerprintimage, or a combination of the fingerprint image and the feature setcorresponding to the fingerprint image.

According to further aspects of the disclosure, storing the referencebiometric images comprises (i) providing a sample biometric image, (ii)providing an additional sample biometric image, (iii) comparing theadditional sample biometric image with each previously-provided samplebiometric image to identify overlapping data in the additional samplebiometric image and each previously-provided sample biometric image,(iv) computing one or more quality measures relating to the additionalsample biometric image and each previously-provided sample biometricimage, (v) comparing each computed quality measure with a thresholdvalue associated with that quality measure, (vi) repeating steps (ii)through (v) until each quality measure meets or exceeds the associatedthreshold value, and (vii) storing the sample biometric images asreference biometric images when each quality measure meets or exceedsthe associated threshold value.

According to further aspects of the disclosure, the quality measurecomprises an amount of unique, non-overlapping data in the additionalsample biometric image and each previously-provided sample biometricimage, and computing an amount of unique data relative to the predefinedimage size.

According to further aspects of the disclosure, the method furthercomprises the step of arranging the additional sample biometric imageand each previously-provided sample biometric image with theiroverlapping portions aligned, and the quality measure comprises the areaof the bounding border encompassing the arranged biometric imagesrelative to the predefined image size.

According to further aspects of the disclosure, the method furthercomprises the step of storing relative location information for two ormore of the plurality of reference biometric images in the referencedatabase.

According to further aspects of the disclosure, the method furthercomprises the steps of comparing a test biometric image with one or moreof the reference biometric images to identify overlapping data in thetest biometric image and each of the one or more reference biometricimages, computing a cumulative amount of overlapping data in the testbiometric image and the one or more reference biometric images, andverifying the user's identity based on the cumulative amount ofoverlapping data in the test biometric image and all of the referencebiometric images.

According to further aspects of the disclosure, the method furthercomprises the step of generating the test biometric image with abiometric sensor.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

According to further aspects of the disclosure, the method furthercomprises the step of verifying the user's identity based on the numberof reference biometric images with which the test biometric image hasoverlapping data.

Further aspects of the disclosure are embodied in a method for verifyinga user's identity based on a comparison of a test biometric image of apredefined image size obtained from the user with reference biometricimage data stored in a reference database. The method comprises thesteps of comparing the test biometric image with one or more of thereference biometric images to identify overlapping data in the testbiometric image and each of the one or more reference biometric images,computing a cumulative amount of overlapping data in the test biometricimage and the one or more reference biometric images, and verifying theuser's identity based on the cumulative amount of overlapping data inthe test biometric image and all of the reference biometric images.

According to further aspects of the disclosure, the method furthercomprises the step of generating the test biometric image with abiometric sensor.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface and wherein each reference biometric imagecomprises a fingerprint image, a feature set corresponding to thefingerprint image, or a combination of the fingerprint image and thefeature set corresponding to the fingerprint image.

According to further aspects of the disclosure, the method furthercomprises the step of verifying the user's identity based on the numberof reference biometric images with which the test biometric image hasoverlapping data.

Further aspects of the disclosure are embodied in a method for verifyinga user's identity based on a comparison of a test biometric imageobtained from the user with reference biometric data stored in areference database. The reference biometric data comprises a pluralityof reference biometric images of different portions of a surface of anorganic tissue of the user, and each reference biometric image partiallyoverlaps at least one other reference biometric image. The referencebiometric data further comprises relative location information betweeneach reference biometric image and at least one other referencebiometric image. The method comprises the steps of comparing the testbiometric image with one or more of the reference biometric images toidentify a matching reference image having overlapping data with thetest biometric image, determining relative location information betweenthe test biometric image and the matching reference image, estimatingareas of overlap between the test biometric image and the remainingreference biometric reference images based on the relative locationinformation between the test biometric image and the matching referenceimage and the relative location information of the reference biometricdata, and determining an amount of matching between the test biometricimage and each of the remaining reference biometric images in eachestimated area of overlap.

According to further aspects of the disclosure, the method furthercomprises the steps of computing a cumulative amount of overlapping datain the test biometric image and the reference biometric images andverifying the user's identity based on the cumulative amount ofoverlapping data in the test biometric image and all of the referencebiometric images.

According to further aspects of the disclosure, the method furthercomprises generating the test biometric image with a biometric sensor.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

According to further aspects of the disclosure, the method furthercomprises the step of verifying the user's identity based on the numberof reference biometric images with which the test biometric image hasoverlapping data.

Aspects of the disclosure are further embodied in a method for verifyinga user's identity based on a comparison of a test biometric imageobtained from the user with reference biometric data stored in areference database. The reference biometric data comprises a pluralityof reference biometric images of different portions of a surface of anorganic tissue of the user, and each reference biometric image partiallyoverlaps at least one other reference biometric image. The referencebiometric data further comprises relative location information betweeneach reference biometric image and at least one other referencebiometric image. The method comprises the steps of comparing the testbiometric image with each of the reference biometric images to identifymatching reference images having overlapping data with the testbiometric image, determining relative location information between thetest biometric image and each of the matching reference images,determining relative location information between each of the matchingreference images based on the relative location information between thetest biometric image and each of the matching reference images, andcomparing the relative location information between each of the matchingreference images determined based on the relative location informationbetween the test biometric image and each of the matching referenceimages with the relative location information of the reference biometricdata.

According to further aspects of the disclosure, the method furthercomprises the steps of computing a cumulative amount of overlapping datain the test biometric image and the reference biometric images andverifying the user's identity based on the cumulative amount ofoverlapping data in the test biometric image and all of the referencebiometric images.

According to further aspects of the disclosure, the method furthercomprises the step of generating the test biometric image with abiometric sensor.

According to further aspects of the disclosure, the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

Other features and characteristics of the subject matter of thisdisclosure, as well as the methods of operation, functions of relatedelements of structure and the combination of parts, and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form partof the specification, illustrate various, non-limiting embodiments ofthe present invention. In the drawings, common reference numbersindicate identical or functionally similar elements.

FIG. 1 is a schematic diagram of a biometric system according to anembodiment of the present invention.

FIG. 2 is a top level schematic illustration of a biometric verificationprocess according to an embodiment of the present invention.

FIG. 3 is a flow chart of an enrollment process according to anembodiment of the present invention.

FIGS. 4A-7B illustrate an exemplary enrollment process according to anembodiment of the present invention.

FIG. 8 is a flow chart of a verification process according to anembodiment of the present invention.

FIG. 9 is a flow chart of a verification process according to anotherembodiment of the present invention.

FIGS. 10A-15D illustrate an exemplary verification process according toan embodiment of the present invention.

FIG. 16 is a graphic representation of a set of reference images storedin a reference database, including spatial correspondence between thereference images.

FIG. 17 is a graphic representation of a partial match between a testimage and a reference image, including the spatial correspondencebetween the test image and the reference image.

FIG. 18 is a graphic representation of a first alternative for employingrelative location information of the reference images and the test imageduring the verification process.

FIG. 19 is a graphic representation of a second alternative foremploying the relative location information of the reference images andthe test image during the verification process.

DETAILED DESCRIPTION

While aspects of the subject matter of the present disclosure may beembodied in a variety of forms, the following description andaccompanying drawings are merely intended to disclose some of theseforms as specific examples of the subject matter. Accordingly, thesubject matter of this disclosure is not intended to be limited to theforms or embodiments so described and illustrated.

Unless defined otherwise, all terms of art, notations and othertechnical terms or terminology used herein have the same meaning as iscommonly understood by one of ordinary skill in the art to which thisdisclosure belongs. All patents, applications, published applicationsand other publications referred to herein are incorporated by referencein their entirety. If a definition set forth in this section is contraryto or otherwise inconsistent with a definition set forth in the patents,applications, published applications, and other publications that areherein incorporated by reference, the definition set forth in thissection prevails over the definition that is incorporated herein byreference.

Unless otherwise indicated or the context suggests otherwise, as usedherein, “a” or “an” means “at least one” or “one or more.”

This description may use relative spatial and/or orientation terms indescribing the position and/or orientation of a component, apparatus,location, feature, or a portion thereof. Unless specifically stated, orotherwise dictated by the context of the description, such terms,including, without limitation, top, bottom, above, below, under, on topof, upper, lower, left of, right of, in front of, behind, next to,adjacent, between, horizontal, vertical, diagonal, longitudinal,transverse, radial, axial, etc., are used for convenience in referringto such component, apparatus, location, feature, or a portion thereof inthe drawings and are not intended to be limiting.

Furthermore, unless otherwise stated, any specific dimensions mentionedin this description are merely representative of an exemplaryimplementation of a device embodying aspects of the disclosure and arenot intended to be limiting.

FIG. 1 is a block schematic diagram of an embodiment for a biometricenrollment and verification system 100. System 100 includes an imagingdevice 105, a processor 110, an input/output (I/O) communication system115, a nonvolatile memory 120 and a RAM memory 125, with memory 120 andmemory 125 collectively defining a memory system 130. System 100 isdescribed as biometric verification system, wherein the system attemptsto measure a correspondence between test biometric information andreference biometric information (one-on-one) in order to confirmidentity of the test biometric information to the reference biometricinformation stored in a reference database. In the present context,“reference biometric information” or “reference biometric data” refersto biometric information or data, such as reference fingerprint data,that is stored for the various enrollees of the system, and “testbiometric information” or “test biometric data,” such as testfingerprint data, is the information that is provided to gain access andis compared to the reference biometric information to assesscorrespondence between the test biometric information and the referencebiometric information to determine if the test biometric informationcorresponds to reference biometric information of one of the enrollees.

Processor 110 may include one or more central processing units (CPUs),such PC microprocessors or workstations interconnected to various othercomponents, such as by a system bus (not shown). Exemplary PCmicroprocessors or workstations include the RISC System/6000 seriesavailable from International Business Machines Corporation (IBM)(RS/6000) (RISC System/6000 is a trademark of International BusinessMachines Corporation).

Imaging device 105 provides image data of an organic tissue, such as afingerprint; either directly (i.e., it comprises a sensor or imager thatgenerates image data) or by accessing a data structure or memory toobtain previously generated and stored image data. The image data may beof a reference fingerprint, i.e., reference biometric data, or of afingerprint-under-test, i.e., test biometric data. Sensors that may beused as an imaging device 105 with system 100 for generating biometricimage data include charge-coupled devices (CCD), complementary metaloxide semiconductor (CMOS), capacitive imaging devices, or other imagegenerating devices. System 100 uses a fingerprint image, or otherbiometric image data, provided by the imaging device 105. In some cases,imaging device 105 may preprocess images, such as performing imagekeystone corrections (a geometric correction used to account for opticaldistortions associated with optical/prism based systems) when returningan image size proportionate to fingerprint size or perform imagereconstruction to assemble an image taken in bands as a finger is‘swiped’ across the sensor.

An operating system runs on processor 110, providing control andcoordinating the functions of the various components of the system. Theoperating system may be one of the commercially available operatingsystems such as the AIX 6000 operating system or OS/2 operating systemavailable from IBM (AIX 6000 and OS/2 are trademarks of IBM),Microsoft's Windows, Apple's MacOS, Google's Android, as well as UNIXand AIX operating systems. Custom programs, controlled by the system,are moved into and out of memory. These programs include the programdescribed below in combination with programs for analyzing and comparingfingerprint-related data. Imaging device 105, I/O communication system115, and memory system 130 is coupled to processor 110 via a bus andwith memory system 130 including a Basic Input/Output System (BIOS) forcontrolling the basic system functions.

An input/output (“I/O”) communication system 115 interconnects system100 with outside devices or networks, enabling the system to communicatewith other such systems over a communications medium (e.g., directlywired, Local Area Network (LAN), or Wide Area Network (WAN), whichincludes, for example, the Internet, the WEB, intranets, extranets, andother public and private networks, wired, optical, or wireless). Theterms associated with the communications system are meant to begenerally interchangeable and are so used in the present description ofthe distribution network. In various embodiments, I/O devices (notshown) may also be connected to the system bus via I/O communicationsystem 115. For example, a keyboard, a pointing device (e.g., mouse,trackball, or other pointing device), and a display or visual orauditory indicator may be interconnected to system 100 through I/Ocommunication system 115. It is through such input devices that the usermay interactively relate to the programs for manipulating the resources,images, subsystems, processes, and system described herein. By using theaforementioned I/O devices, a user is capable of inputting informationto the system, e.g., through the keyboard or mouse, and receive outputinformation from the system, e.g., from a display or indicator. Thesystem may contain a removable memory component for transferring images,maps, instructions, or programs.

In an embodiment, system 100 compares image data for a live finger(i.e., test biometric information) to known (enrolled) image data (i.e.,reference biometric information) stored in memory system 130. Theenrollee databases of conventional biometric verification systemstypically includes one reference image data from each finger of theenrollee. In an embodiment, system 100 stores multiple reference imagesfrom each finger, preferably of different parts of the each fingersurface. In an embodiment, when comparing test image data for a livefinger against reference image data in the database 130, system 100tests the image data for the live finger (i.e., test biometric data ortest image data) against multiple reference images from the same finger.

FIG. 2 is a top level schematic illustration of an embodiment of abiometric enrollment and verification process 200. Process 200, executedby system 100 for example, includes three main processes: an imagecapture process 201, an enrollment process 202, and a verificationprocess 203. During the image capture process 201 image data of anorganic tissue, e.g., a fingerprint of a user, is provided, e.g., byimaging device 105, either directly from a sensor or imager thatgenerates image data or by accessing a data structure or memory toobtain the image data. The image capture process 201 may preprocessimages, such as by performing image keystone corrections (a geometriccorrection used to account for optical distortions associated withoptical/prism-based systems) when returning an image size proportionateto fingerprint size or by performing image reconstruction to assemble animage from partial images generated as a finger is moved across thesensor.

The next step in the biometric enrollment and verification process 200is an enrollment process 202 for enrolling the biometric informationfrom the image data captured in image capture process 201 into areference database 204 of the user's reference biometric information, aswill be described in further detail below with reference to FIGS. 3-7.When the enrolment process 202 is successfully completed, a user'sbiometric reference data is stored in the biometric enrollment andverification system, and the user is enrolled or registered with thesystem.

When the user later requests access to a resource or location connectedto the biometric enrollment and verification system, new biometric data(e.g., test image data) of the user's organic tissue is provided by theimaging device 105 during the image capture process 201, and the testimage data is compared against the reference biometric information(e.g., reference image data) stored in the reference database 204 duringthe verification process 203. If a sufficient match between thereference image data and the test image data is found, the user isauthorized access to the resource or location. The details of theverification process 203 are described in further detail below withreference to FIGS. 8-15.

FIG. 3 is a flow chart of an embodiment of an enrollment process 202.The first step 301 of the enrollment process 202 is to collect a firstsample image and a second sample image of the organic tissue for theprospective enrollee, e.g., by the image capture process 201. Then, instep 302 data of the second sample image is compared with data of thefirst sample image with a suitable matching algorithm to identifymatching, or overlapping, portions of data comprising the two images.The matching algorithm aligns the two sample images by superimposing thedata of the first and second sample images in such a manner that thematching or overlapping portions of the data of images coincide witheach other, or, are “aligned” with each other. For example, alignment oftwo or more fingerprint images would superimpose the images so that theportions of the fingerprints captured in each image that are identicalwith portions of the fingerprints captured in the other image coincidewith one another. With the two images aligned, the matching algorithmmeasures a relative location/position/orientation (e.g., translation androtation) between the two sample images.

The matching algorithm may be any suitable matching algorithm, such as,a feature-based algorithm, a pattern-based algorithm, or a combinationof feature-based and pattern-based algorithms. Feature based algorithmsoperate by first extracting a feature set from each of the sample imagesand by then comparing the feature sets against each other. Exemplaryfingerprint features include, but are not limited to, minutiae (i.e.,identifying characteristics of the fingerprints), fingerprintridge/curvature, and combinations thereof. Pattern-based algorithmscompare the patterns of each sample image against each other by imageprocessing tools in real space or frequency space, such as, rotationalinvariant cross-correlation techniques and frequency-based methods usingFast Fourier Transform, Discrete Fourier Transform, and Wavelets.

Exemplary fingerprint matching algorithms are described in Handbook ofFingerprint Recognition by Davide Maltoni, Dario Maio, Anil K. Jain, andSalil Prabhakar (ISBN 978-1-84882-254-2).

The degree of overlap that is considered to be a “match” varies fromalgorithm to algorithm and also with the sensor size (image size).Minutiae-based recognition algorithms typically require a larger degreeof overlap than pattern-based algorithms, simply due to a low density ofminutiae points in some regions in many fingerprints. For apattern-based matching algorithm to work, the common region typicallyneeds to span 4-8 ridges, implying a common region of approximately 3×3mm. Cross-correlation techniques, including phase correlation, typicallyneeds a common area of at least 25% between two images to work robustly.

When the matching process is completed the sample images are stored inthe reference database 204 as reference biometric data in the form ofreference images. Each reference image may comprise an individual sampleimage, a feature set corresponding to the sample image, or a combinationof the sample image and the feature set corresponding to the sampleimage. If one sample image does not contain unique information (i.e., itis partially or wholly redundant with a previously-stored referenceimage), it may be discarded to save memory resources. In variousembodiments, the data relating to the relativelocation/position/orientation between the reference image and one ormore other reference images is also stored in the reference database204.

The third step 303 of the enrollment process 202 is to calculate aquality measure of the reference images created in step 302. Thiscalculation is described below.

The enrollment process then continues to step 304 where it is decided ifthe enrollment process is completed for the prospective enrollee. In oneembodiment, the enrollment process 202 stops when the quality measure ofthe reference images calculated for the prospective enrollee in step 303exceeds a predefined quality threshold. In addition, it is also possibleto take into consideration the number of images and/or feature sets inthe reference database for the prospective enrollee. Thus, in oneembodiment the enrollment process 202 may stop when the quality measureof the reference image data for the prospective enrollee exceeds thepredefined quality threshold or when the number of images and/or featuresets in the reference database for the prospective enrollee exceeds apredefined minimum number of images. If it is determined that theenrollment process 202 is not completed for the prospective enrollee,the processes returns to step 301 to collect a third sample image of theprospective enrollee. In step 302, the third sample image is comparedwith all reference images for the prospective enrollee in the referencedatabase 204, e.g., the first and second reference images, to align thethird sample image with the previously-stored reference image(s) and tocompute a relative location/position/orientation between the thirdsample image and each reference image.

Thereafter, the third sample image (i.e., the image itself and/or afeature set of the third sample image) is stored, optionally along withdata relating to the location/position/orientation of the third sampleimage with respect to other reference images, in the reference database204 as additional biometric reference data Steps 303 and 304 arerepeated and if the predefined quality threshold and/or image limit aremet, the enrollment process is complete for the prospective enrollee. Ifthe predefined quality threshold and/or image limit are not met in step304, process 202 will repeat for a fourth (an optionally fifth, sixthseventh, etc.) sample image for the prospective enrollee until thepredefined quality threshold and/or image limit are met or some otherstop parameter is reached.

Calculation of the quality measure, step 303, is described as follows.The quality measure of the reference image(s) may comprise a singlecalculated quality measure, or it may comprise a combination of any twoor more of a plurality of calculated quality measures.

A first quality measure that may be calculated in step 303 is the totalarea of unique image information, A_(unique), that is in the storedreference image(s) for a prospective enrollee. A_(unique), is a measureof the amount of unique information within the biometric reference datafor a particular enrollee relative to the sensor size, which correspondsto the size of a single image. Information or data is “unique” if it isnot already contained in other reference data stored in the referencedatabase 204. In that regard, “unique” data may also be thought of asnon-redundant data. For example, if the database consists of onereference image for a prospective enrollee, then A_(unique)=1, since theentire reference image corresponding to the size of the sensor consistsof unique data. If the reference database 204 consist of two identicalreference images for the prospective enrollee, then A_(unique)=1 sincethe amount of unique data contained in the cumulative data of the twoimages still corresponds to the size of the sensor (i.e., a singleimage). On the other hand, if the reference database 204 consists of tworeference images have no overlapping areas, then A_(unique)=2 since theamount of unique data corresponds to two images, i.e., twice the size ofthe sensor.

A second quality parameter of the reference images that may be computedin step 303 for a prospective enrollee is the area of a bounding box,A_(box), encompassing a cluster of aligned reference images relative tothe sensor size. If there are more than one cluster of reference images,the area of the bounding box encompasses the largest cluster ofreference images. For example, FIG. 5(a) shows two reference images 501,502 that are aligned with one another and have an area of matching oroverlapping data represented by area 503. A_(unique) for images 501, 502would be the area of image 501, plus the area of image 502, less thearea of overlap 503. Note that, as aligned, image 502 is shifted up andto the right relative to image 501. The bounding box corresponds to box504 that encompasses the aligned images 501, 502, and A_(box) is thearea of box 504. While box 504 is substantially square, it is not arequirement that the box encompassing the reference images be square oreven rectangular. A bounding “box” may be a bounding border, or a convexhull or convex envelope, of any shape, e.g., square, rectangular, oval,circular, triangular, polygonal, etc., which encompasses all thereference images and for which an area of the encompassing border can bedetermined.

To ensure that the data of a test image of an enrolled user correspondsto reference data stored for that user, the reference images stored forthe enrolled user encompass a substantial part of the user's finger thatis larger (possibly much larger) than the size of the sensor (i.e., asingle image), and there are no data gaps between reference images.

To ensure that the reference database does not have large holes, orareas with missing information, is it useful to calculate a thirdquality measure, the compactness of the cluster of reference images. Ifthere is more than one cluster for reference images, then thecompactness is a measure of the compactness of the largest of theclusters of reference images. The compactness of an enrollee's referencedata in the reference database, hereinafter referred to as Compactness,is defined as the ratio of total area of unique information contained inthe reference images stored for the enrollee to the area of the bondingbox encompassing the largest cluster of reference images, that isA_(unique)/A_(box).

Since the area of unique data can never exceed the area of a boxbounding the reference images, A_(unique)/A_(box) can never be greaterthan 1.0. As A_(unique)/A_(box) approaches 1.0, this means that a largerproportion of the data contained within a box bounding the referenceimages is unique data, and thus there are relatively few gaps in thereference data. On the other hand, as A_(unique)/A_(box) becomes moreand more less than 1.0, this means that a smaller proportion of the datacontained with a box bounding the reference images is unique data, andthus there may be relatively large gaps in the unique data in thereference data. Thus, it is preferable that the compactnessA_(unique)/A_(box) be close to 1.0.

FIGS. 4-7 illustrate an exemplary enrollment process where thresholds ofthe quality measure of the reference database are set to A_(unique)>3(relative to sensor size), and Compactness>0.8. For ease of illustrationonly, the reference images are shown as a stitched reference image, butstitching reference images is not necessarily required in the processdescribed herein. On the contrary, in one embodiment it is onlynecessary to measure the relative locations of the aligned referenceimages; it is not necessary to stitch the reference images together. Themeasurement of relative location involves calculating the translation(dx, dy) and rotation (dΘ) between the reference images.

The relative locations, or spatial correspondences between the images,can be used to guide the prospective enrollee during the enrollmentprocess 202. Knowing where each reference image belongs relative to theother reference images enables the system 100 to calculate the totalsize of the unique image data enrolled so far in the image database 204.Also the total image information enrolled so far can be quantified interms of quality measures, such as, compactness, size of region withholes (i.e., lack of unique data), etc. This information can be used instep 304 to determine when the enrollment process 202 is complete.

FIG. 4(a) show the result after the first reference image, A_(unique)=1(the amount of unique information is equal to the sensor size),A_(box)=1, and Compactness A_(unique)/A_(box)=1. FIG. 4(b) shows abounding box, which, in this case corresponds to the single image orsensor, on an x-y Cartesian coordinate system with an asterisks (*)indicating the center of the single image.

FIG. 5(a) shows the result after a second, partially overlappingreference image 502 is combined with first reference image 501 in thereference database. FIG. 5(b) shows the bounding box 504 and the centers(*) of reference images 501, 502 in the Cartesian coordinate system. Theportion of image 502 overlapping with image 501, as represented byrectangle 503 in FIG. 5(a), is not unique as that data was stored in thereference database as part of image 501, but the portion of second image502 that is outside the rectangle 503 is unique. Thus, the amount ofunique data exceeds the amount of data that is stored in a single image,and A_(unique)=1.43. The area of bounding box 504 that encompasses thealigned images 501, 502, A_(box)=1.51, and the CompactnessA_(unique)/A_(box)=0.95.

FIG. 6(a) shows the result after a third reference image, partiallyoverlapping the first and second reference images is combined with firstand second reference images in the reference database. FIG. 6(b) showsthe bounding box and the centers (*) of first, second, and thirdreference images in the Cartesian coordinate system. For the resultsshown in FIGS. 6(a) and 6(b), A_(unique)=1.53, A_(box)=1.61, andCompactness A_(unique)/A_(box)=0.95.

FIGS. 7(a) and 7(b) show the result after the quality measure thresholdsof the reference database were exceeded and the enrollment was completedafter 15 sample images had been captured. Here A_(unique)=3.2,A_(box)=3.51, and Compactness A_(unique)/A_(box)=0.91. In exemplary datacapture shown in FIG. 7, nine of the sample images were discarded andnot saved as reference images as they did not contain uniqueinformation, thus the final reference database consist of six (D_(ri)=6)individual reference images. The final reference data stored in thereference database for the enrollee may consist of six individualgrayscale images, six individual feature sets, or a combination of thegrayscale images and feature sets.

FIG. 8 is a flow chart of an embodiment of a verification process 203.The first step 501 of the verification process 203 is to capture testbiometric data in the form of a test image, e.g., by the image captureprocess 201. Then, in step 502, the test image is aligned with referenceimage r_(n), where 1≤n≤D_(ri), from reference database 204 using asuitable matching algorithm (where D_(ri) is the total number ofreference images in the reference database 204 for an enrollee). Wherethe biometric information is fingerprint image data, D_(ri) may refer tothe number of reference fingerprint images for each finger—which may notbe the same for each finger—of each enrollee in the system. The matchingalgorithm may be a feature-based algorithm, a pattern-based algorithm,or a combination of feature-based and pattern-based algorithms.Feature-based algorithms are based on first extracting a feature setfrom each of the sample images and then comparing the feature setsagainst each other. Exemplary fingerprint features includes, but are notlimited to, minutiae, fingerprint ridge/curvature, and combinationsthereof. Pattern-based algorithms compare the patterns of each sampleimage against each other by image processing tools in real space orfrequency space, such as rotational invariant cross-correlationtechniques and frequency based methods using Fast Fourier Transform,Discrete Fourier Transform, and Wavelets.

When the alignment (step 502) is completed, process 203 continues tostep 503 where a match score between the test image and reference imager_(n) is calculated. In one embodiment, the match score is thepercentage of the test image data that matches the reference image datar_(n).

The verification process 203 then continues to step 504 where a totalmatch score is calculated. In one embodiment, the total match scoreequals the percentage of the test image data that matched the referenceimage data in the reference database 204. That is, the total match scoreis the total, cumulative percentage of matching unique image databetween the test image and all the reference images in the referencedatabase 204.

In step 505, the verification process checks if the total match scoreexceeds a predetermined global threshold. If the total match scoreexceeds the global threshold, then the verification process 203 issuccessful and the user is authorized access to the resource orlocation. If the total match score does not exceed the global threshold,then the verification process 203 continues to step 506.

In step 506, the verification process checks if there are more referenceimages in the reference database 204 to match against the test image,that is, if n<D_(ri). If more reference images are available, then n isincreased to n+1 and the process returns to step 502. If, on the otherhand, n=Dri, then the verification process 203 is unsuccessful, and theuser is denied access to the resource or location.

In one embodiment, the relative locations between the reference imagesor feature sets are discarded when the enrollment process is completed.In another embodiment, the relative locations between the referenceimages or feature sets are stored in the reference database. In anembodiment where the relative locations between the reference images orfeature sets are stored in the reference database 204, another measuremay be included in the total match score, a measure of the relativelocation between the matching test image and reference images. Asmentioned above, for the verification process to be successful, therelative locations measured in the verification process have to bewithin a predefined threshold of the relative locations stored in thereference database.

For example, during the verification process a test image might matchreference images 1 and 2. The relative location information from theenrollment process indicates that these two reference images also matcheach other and are rotated 30 degrees with respect to each other. Therelative location information also indicates that reference images 1 and2 are translated with respect to each other by 30 pixels in theX-direction and by 50 pixels in the Y-direction. If this relativelocation information is not compatible with the results from matchingthe test image with the same two reference images, the matching resultsare likely to be incorrect. Thus, the stored relative locationinformation works as a second verification step that can make thematches more robust.

In principle one test image can match many reference images, whichstrengthens the matching result. However, in many cases the overlapbetween test and reference images is really small, making the overallmatching result uncertain. Thus, it various embodiments, it isadvantageous to have a final step where all location information fromthe verification process is compared with all relevant locationinformation from the enrollment process

In a further embodiment, the total match score also includes a measureof the number of reference images with a successful match with the testimage. This additional measure may add additional security in case thereis a very good match between the test image and only one reference imageand no match with any of the other reference images. In this case, theone very good match may result in a total match score that exceeds theglobal threshold of the total percentage of matching unique imageinformation. If the test image is much smaller than the area of aregular fingerprint, it is possible that the test image is not a verygood match with the rest of the reference images in the referencedatabase 204. When a second threshold of a minimum number of successfulreference image matches is included in the total match score, thepossibility of falsely verifying the test image is greatly reduced.

FIG. 9 is a flow chart of an alternative embodiment of a verificationprocess 203′ that involves more than one threshold comparison forincreased security. As in verification process 203 of FIG. 8, first step501 of the verification process 203′ is to capture test biometric datain the form of a test image, e.g., by the image capture process 201.Then in step 502 the test image is aligned with reference image r_(n),where 1≤n≤D_(ri), from reference database 204 using a suitable matchingalgorithm (where D_(ri) is the total number of reference images in thereference database 204).

When the alignment (step 502) is completed, process 203′ continues tostep 503 where a match score between the test image and reference imager_(n), is calculated. For process 203′, the match score calculated instep 503 will be referred to as an image match score, as it is the matchscore for just the single reference image r_(n).

In step 601, the image match score between the test image and referenceimage r_(n) is compared to a local threshold. If the image match scoredoes not meet or exceed the local threshold, process 203′ proceeds tostep 506 to check if there are more reference images in the referencedatabase 204 to match against the test image, that is, if n<D_(ri). Ifmore reference images are available, then n is increased to n+1 and theprocess returns to step 502. If, on the other hand, n=Dri, then theverification process 203 is unsuccessful, and the user is denied accessto the resource or location.

If the image match score does meet or exceed the local threshold,verification process 203′ then continues to step 504 where a total matchscore is calculated.

In step 505, the verification process 203′ checks if the total matchscore meets or exceeds the global threshold. If the total match scoremeets or exceeds the global threshold, then access is granted. If thetotal match score does not meet or exceed the global threshold, then theverification process 203′ continues to step 506 to check if there aremore reference images in the reference database 204 to match against thetest image, that is, if n<D_(ri). If more reference images areavailable, then n is increased to n+1 and the process returns to step502. If, on the other hand, n=Dri, then the verification process 203 isunsuccessful, and the user is denied access to the resource or location.

Step 601 improves the efficacy of the verification process 203′ as steps504 and 505 are performed only if there is a sufficient match betweenthe test image and the particular reference image r_(n) so that theimage match score meets or exceeds the local threshold.

FIGS. 10-15 illustrate an exemplary verification process using theexemplary reference database created with reference to FIGS. 4-7.

FIG. 10 visually illustrates the result of a match process between atest image (FIG. 10(a)) and a first reference image r₁ (FIG. 10(b)).Overlap between reference image r₁ and the test image is shown in theFIG. 10(d). Matching features between the two images are shown in solidlines, non-matching features of the test image are shown in dashedlines, and non-matching features of the first reference image are shownin greyscale. FIG. 10(c) illustrates the cumulative matching of the testimage with the reference image, again, with matching features shown insolid lines, nonmatching features of the test image shown in dashedlines, and non-matching features of the reference image shown ingreyscale. That is, FIG. 10(c) is a graphic representation of the totalmatch score between the test image (FIG. 10(a)) and all reference imagesof the reference database that have thus far been tested (i.e.,reference image FIG. 10(b)). After the first reference image, the totalmatch score is identical to the match score (of, e.g., 22% in FIG. 10)between the test image and reference image r₁ (FIG. 10(b)).

FIG. 11 visually illustrates the result of a match process between thesame test image (now shown in FIG. 11(a)) and a second reference imager₂ (FIG. 11(b)). Overlap between reference image r₂ and the test imageis shown in the FIG. 11(d) with matching features indicated by solidlines, non-matching features of the test image indicated by dashedlines, and non-matching features of the second reference image shown ingreyscale. FIG. 11(c) now shows a total match score of 56% between thetest image (FIGS. 10(a) and 11(a)) and reference images r₁ (FIG. 10(b)and r₂ (FIG. 11(b)).

FIG. 12 visually illustrates the result of a match process between thesame test image (now shown in FIG. 12(a)) and a third reference image r₃(FIG. 12(b)). Overlap between reference image r₃ and the test image isshown in FIG. 12(d) with matching features indicated by solid lines,non-matching features of the test image indicated by dashed lines, andnon-matching features of the third reference image shown in greyscale.FIG. 12(c) now shows a total match score of 87% between the test image(FIGS. 10(a), 11(a), 12(a)) and the three reference images r₁ (FIG.10(b)), r₂ (FIG. 11(b)), and r₃ (FIG. 12(b)).

FIG. 13 visually illustrates the result after a match process betweenthe same test image (now shown in FIG. 13(a)) and a fourth referenceimage r₄ (FIG. 13(b)). Overlap between reference image r4 and the testimage is shown in FIG. 13(d) with matching features indicated by solidlines, non-matching features of the test image indicated by dashedlines, and non-matching features of the fourth reference image shown ingreyscale. In the case of the match process with the fourth referenceimage shown in FIG. 13, there is not sufficient overlap between the testimage and the reference image r₄ to align the images. Hence, FIG. 13(c)shows an unchanged total match score of 87% between the test image(FIGS. 10(a), 11(a), 12(a), 13(a)) and the four reference images r₁(FIG. 10(b)), r₂ (FIG. 11(b)), r₃ (FIG. 12(b), and r₄ (FIG. 13(b)) ascompared to the total match shown in FIG. 12(d).

FIG. 14 visually illustrates the result of the match process between thesame test image (now shown in FIG. 14(a)) and the fifth reference imager₅ (FIG. 14(b)). Overlap between reference image r₅ and the test imageis shown in FIG. 14(d) with matching features indicated by solid lines,non-matching features of the test image indicated by dashed lines, andnon-matching features of the reference image shown in greyscale. FIG.14(c) now illustrates a total match score of 99% between the test image(FIGS. 10(a), 11(a), 12(a), 13(a), 14(a)) and the five reference imagesr₁ (FIG. 10(b)), r₂ (FIG. 11(b)), r₃ (FIG. 12(b), r₄ (FIG. 13(b), and r₅(FIG. 14(b)).

FIG. 15 visually illustrates the result of the match process between thesame test image (FIG. 15(a)) and the sixth reference image r₆ (FIG.15(b)). Overlap between reference image r₆ and the test image is shownin FIG. 15(d) with features matching the test image indicated by solidlines, non-matching features of the test image indicated by dashedlines, and non-matching features of the sixth reference image shown ingreyscale. As was the case for reference image r₄ (FIG. 13), there isnot enough overlap between the test image and the sixth reference imager₆ to align the images and calculate a match score. Hence, the FIG.15(c) shows an unchanged and final total match score of 99% between thetest image and the six reference images of the reference database.

In one embodiment, the verification process 203 may keep track of thenumber of times a reference image is successfully matched with a testimage. Then, if a reference image has not been successfully matched witha test image after a predetermined number of test images, theverification process may remove the reference image from the referencedatabase 204.

In one embodiment, the verification process 203 may evaluate the qualityof test images that are verified by the verification process 203. Onemeasure of high quality could be a match score with all of the referenceimages in the reference database. A test image might match almost allother images but the overlap with each image could be relatively small.Thus, when the verification process 203 finds a high quality test image,the verification process 203 may add the test image as a reference imageto the reference database. The high quality test image contains “new”image information condensed into one individual template image, andadding that image to the reference database will in general increase theprobability of a correct match.

In various embodiments, the relative location information can be used inthe verification process 203 where a certain test image is matchedagainst all enrolled reference images. Knowing the spatialcorrespondences between the reference images from the enrollment process202, the matching result between the test image and the reference imagescan be checked with respect to consistency. For instance, if a match isfound between a test image and several reference images, but therelative placement detected among the reference images matching the testimage is not consistent with the location information from theenrollment process 202, the match may be disregarded.

Various alternatives for employing the relative location information ofthe reference images and the test image during the verification processare illustrated in FIGS. 16-19.

FIG. 16 is a graphic representation of a set of reference images 1-5stored in a reference database. The reference images themselves arestored, as is the relative location information, or spatialcorrespondence, of the reference images with respect to each other.

FIG. 17 is a graphic representation of a partial match between a testimage and reference image 2, including the spatial correspondencebetween the test image and reference image 2.

FIG. 18 is a graphic representation of a first alternative for employingthe relative location information of the reference images and the testimage during the verification process. Since spatial correspondencebetween the test image and reference image 2 is known and since spatialcorrespondence between reference image 2 and each of the referenceimages 1 and 3-5 is known, the spatial correspondences between the testimage and all other reference images 1 and 3-5 are then, in principle,also known so that areas of overlap between the test image and the otherreference images can be predicted, or estimated. All the other referenceimages 1 and 3-5 can be placed roughly on top of the test image and aquick check can be performed to determine if they match where they aresupposed to. By matching the test image to the other reference images inthis manner, the matching process will be faster as the system need notsearch each reference image in its entirety for a match.

FIG. 19 is a graphic representation of a second alternative foremploying the relative location information of the reference images andthe test image during the verification process. The test image ismatched against all other reference images 1-5 without using therelative location information from the enrollment process. When thematching is completed, a consistency check is performed where the knownrelative locations between the reference images are checked againsttheir relative placements after matching the test image. In the exampleshown in FIG. 19, the matching between the test image and referenceimage no. 3 is not consistent with the relative location informationfrom the enrollment process, which, in the illustrated matching process,leaves a perceived gap—shown in cross-hatching—where the test image doesnot match any of the reference images. This may result in a matchingscore that will then be a bit lower but not by much since the entiretest image is still very well matched by the remaining four templateimages 1-2 and 4-5. Only the hatch region of the test image is notmatched to any reference image.

A further alternative would be to not us relative location informationduring the verification process, in which case the relative locationinformation may be discarded or not stored in the enrollment process.

EXEMPLARY EMBODIMENTS Embodiment 1

A biometric identification method comprising storing a plurality ofreference biometric images of an organic tissue of a user in a referencedatabase, wherein each of the reference biometric images has apredefined image size and at least partially overlaps at least one ofthe other reference biometric images, and wherein all of the referencebiometric images arranged with their overlapping portions aligned has anarea greater than the predefined image size.

Embodiment 2

The method of embodiment 1, wherein storing the reference biometricimages comprises: providing a plurality of sample biometric images ofthe predefined image size from the user; comparing each of the samplebiometric images with the other sample biometric images to identifyoverlapping data in the sample biometric images; computing an amount ofunique, non-overlapping data in the sample biometric images; computingan amount of unique data relative to the predefined image size;arranging the plurality of biometric images with their overlappingportions aligned and computing the area of a bounding borderencompassing the arranged biometric images relative to the predefinedimage size; and storing at least a portion of the plurality of samplebiometric images as a plurality of reference biometric images in thereference database.

Embodiment 3

The method of Embodiment 2, wherein the plurality of reference biometricimages stored in the reference database comprises of number of biometricimages that results in the amount of unique data relative to thepredefined image size being equal to or greater than a first predefinedthreshold, and the area of the bounding border encompassing the arrangedbiometric images relative to the predefined image size is equal to orgreater than a second predefined threshold.

Embodiment 4

The method of Embodiment 3, further comprising computing compactness ofthe plurality of reference biometric images as the amount of unique datarelative to the predefined image size divided by the area of thebounding border encompassing the arranged biometric images relative tothe predefined image size.

Embodiment 5

The method of Embodiment 4, further comprising comparing the compactnesswith a third predefined threshold.

Embodiment 6

The method of any one of Embodiments 1-5, wherein providing the samplebiometric images comprises generating the sample biometric images with abiometric sensor.

Embodiment 7

The method of any one of Embodiments 1-6, wherein the organic tissuecomprises a finger surface and wherein each reference biometric imagecomprises a fingerprint image, a feature set corresponding to thefingerprint image, or a combination of the fingerprint image and thefeature set corresponding to the fingerprint image.

Embodiment 8

The method of any one of Embodiments 1-7, wherein storing the referencebiometric images comprises (i) providing a sample biometric image; (ii)providing an additional sample biometric image; (iii) comparing theadditional sample biometric image with each previously-provided samplebiometric image to identify overlapping data in the additional samplebiometric image and each previously-provided sample biometric image;(iv) computing one or more quality measures relating to the additionalsample biometric image and each previously-provided sample biometricimage; (v) comparing each computed quality measure with a thresholdvalue associated with that quality measure; (vi) repeating steps (ii)through (v) until each quality measure meets or exceeds the associatedthreshold value; and (vii) storing the sample biometric images asreference biometric images when each quality measure meets or exceedsthe associated threshold value.

Embodiment 9

The method of Embodiment 8, wherein the quality measure comprises anamount of unique, non-overlapping data in the additional samplebiometric image and each previously-provided sample biometric image, andcomputing an amount of unique data relative to the predefined imagesize.

Embodiment 10

The method of Embodiment 8, further comprising arranging the additionalsample biometric image and each previously-provided sample biometricimage with their overlapping portions aligned, and wherein the qualitymeasure comprises the area of the bounding border encompassing thearranged biometric images relative to the predefined image size.

Embodiment 11

The method of any one of Embodiments 1-10, further comprising storingrelative location information for two or more of the plurality ofreference biometric images in the reference database.

Embodiment 12

The method of any one of Embodiments 1-11, further comprising: comparinga test biometric image with one or more of the reference biometricimages to identify overlapping data in the test biometric image and eachof the one or more reference biometric images; computing a cumulativeamount of overlapping data in the test biometric image and the one ormore reference biometric images; and verifying the user's identity basedon the cumulative amount of overlapping data in the test biometric imageand all of the reference biometric images.

Embodiment 13

The method of Embodiment 12, further comprising generating the testbiometric image with a biometric sensor.

Embodiment 14

The method of Embodiment 12, wherein the organic tissue comprises afinger surface and the test biometric image comprises a fingerprintimage.

Embodiment 15

The method of Embodiment 12, further comprising verifying the user'sidentity based on the number of reference biometric images with whichthe test biometric image has overlapping data.

Embodiment 16

A method for verifying a user's identity based on a comparison of a testbiometric image of a predefined image size obtained from the user withreference biometric image data stored in a reference database, themethod comprising: comparing the test biometric image with one or moreof the reference biometric images to identify overlapping data in thetest biometric image and each of the one or more reference biometricimages; computing a cumulative amount of overlapping data in the testbiometric image and the one or more reference biometric images; andverifying the user's identity based on the cumulative amount ofoverlapping data in the test biometric image and all of the referencebiometric images.

Embodiment 17

The method of Embodiment 16, further comprising generating the testbiometric image with a biometric sensor.

Embodiment 18

The method of any one of Embodiment 16 or 17, wherein the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

Embodiment 19

The method of any one of Embodiments 16-18, wherein the organic tissuecomprises a finger surface and wherein each reference biometric imagecomprises a fingerprint image, a feature set corresponding to thefingerprint image, or a combination of the fingerprint image and thefeature set corresponding to the fingerprint image.

Embodiment 20

The method of any one of Embodiments 16-19, further comprising verifyingthe user's identity based on the number of reference biometric imageswith which the test biometric image has overlapping data.

Embodiment 21

A method for verifying a user's identity based on a comparison of a testbiometric image obtained from the user with reference biometric datastored in a reference database, wherein the reference biometric datacomprises a plurality of reference biometric images of differentportions of a surface of an organic tissue of the user, wherein eachreference biometric image partially overlaps at least one otherreference biometric image, and wherein the reference biometric datafurther comprises relative location information between each referencebiometric image and at least one other reference biometric image, themethod comprising: comparing the test biometric image with one or moreof the reference biometric images to identify a matching reference imagehaving overlapping data with the test biometric image; determiningrelative location information between the test biometric image and thematching reference image; estimating areas of overlap between the testbiometric image and the remaining reference biometric reference imagesbased on the relative location information between the test biometricimage and the matching reference image and the relative locationinformation of the reference biometric data; and determining an amountof matching between the test biometric image and each of the remainingreference biometric images in each estimated area of overlap.

Embodiment 22

The method of Embodiment 21, further comprising: computing a cumulativeamount of overlapping data in the test biometric image and the referencebiometric images; and verifying the user's identity based on thecumulative amount of overlapping data in the test biometric image andall of the reference biometric images.

Embodiment 23

The method of Embodiment 21 or 22, further comprising generating thetest biometric image with a biometric sensor.

Embodiment 24

The method of any one of Embodiments 21-23, wherein the organic tissuecomprises a finger surface and the test biometric image comprises afingerprint image.

Embodiment 25

The method of any one of Embodiments 21-24, further comprising verifyingthe user's identity based on the number of reference biometric imageswith which the test biometric image has overlapping data.

Embodiment 26

A method for verifying a user's identity based on a comparison of a testbiometric image obtained from the user with reference biometric datastored in a reference database, wherein the reference biometric datacomprises a plurality of reference biometric images of differentportions of a surface of an organic tissue of the user, wherein eachreference biometric image partially overlaps at least one otherreference biometric image, and wherein the reference biometric datafurther comprises relative location information between each referencebiometric image and at least one other reference biometric image, themethod comprising: comparing the test biometric image with each of thereference biometric images to identify matching reference images havingoverlapping data with the test biometric image; determining relativelocation information between the test biometric image and each of thematching reference images; determining relative location informationbetween each of the matching reference images based on the relativelocation information between the test biometric image and each of thematching reference images; and comparing the relative locationinformation between each of the matching reference images determinedbased on the relative location information between the test biometricimage and each of the matching reference images with the relativelocation information of the reference biometric data.

Embodiment 27

The method of Embodiment 26, further comprising: computing a cumulativeamount of overlapping data in the test biometric image and the referencebiometric images; and verifying the user's identity based on thecumulative amount of overlapping data in the test biometric image andall of the reference biometric images.

Embodiment 28

The method of Embodiment 26, further comprising generating the testbiometric image with a biometric sensor.

Embodiment 29

The method of Embodiment 26, wherein the organic tissue comprises afinger surface and the test biometric image comprises a fingerprintimage.

While the subject matter of this disclosure has been described and shownin considerable detail with reference to certain illustrativeembodiments, including various combinations and sub-combinations offeatures, those skilled in the art will readily appreciate otherembodiments and variations and modifications thereof as encompassedwithin the scope of the present disclosure. Moreover, the descriptionsof such embodiments, combinations, and sub-combinations is not intendedto convey that the claimed subject matter requires features orcombinations of features other than those expressly recited in theclaims. Accordingly, the scope of this disclosure is intended to includeall modifications and variations encompassed within the spirit and scopeof the following appended claims.

The invention claimed is:
 1. A method for verifying a user's identityfrom a test biometric image obtained from the user, the methodcomprising: (A) comparing the test biometric image provided by animaging device with reference biometric data stored in a referencedatabase, wherein the reference biometric data comprises a plurality ofreference biometric images of different portions of a surface of anorganic tissue of the user, wherein each reference biometric imagepartially overlaps at least one other reference biometric image, andwherein the reference biometric data further comprises relative locationinformation between each reference biometric image and at least oneother reference biometric image, and wherein comparing the testbiometric image with reference biometric data comprises using a matchingalgorithm to compare the test biometric image with each of the referencebiometric images to identify matching reference images havingoverlapping data with the test biometric image; (B) determining spatialcorrespondence between the test biometric image and each of the matchingreference images identified in step (A); (C) determining spatialcorrespondence between each of the matching reference images identifiedin step (A) based on the spatial correspondence determined in step (B);(D) comparing the spatial correspondence determined in step (C) with therelative location information of the reference biometric data; (E)computing a cumulative amount of overlapping data in the test biometricimage and the reference biometric images as determined in step (A); and(F) verifying the user's identity based on the cumulative amount ofoverlapping data in the test biometric image and all of the referencebiometric images as determined in step (E).
 2. The method of claim 1,further comprising generating the test biometric image with a biometricsensor.
 3. The method of claim 1, wherein the organic tissue comprises afinger surface and the test biometric image comprises a fingerprintimage.